A Case for Machine Learning to Optimize Multicore Performance

TitleA Case for Machine Learning to Optimize Multicore Performance
Publication TypeConference Paper
Year of Publication2009
AuthorsGanapathi, A., Datta K., Fox A., & Patterson D.
Conference NameHotPar09
Date Published3/2009
Conference LocationBerkeley, CA
Abstract

Multicore architectures have become so complex and diverse that there is no obvious path to achieving good performance. Hundreds of code transformations, compiler flags, architectural features and optimization parameters result in a search space that can take many machine- months to explore exhaustively. Inspired by successes
in the systems community, we apply state-of-the-art machine learning techniques to explore this space more intelligently. On 7-point and 27-point stencil code, our technique takes about two hours to discover a configuration whose performance is within 1% of and up to 18% better than that achieved by a human expert. This factor of 2000 speedup over manual exploration of the auto-tuning
parameter space enables us to explore optimizations that were previously off-limits. We believe the opportunity for using machine learning in multicore autotuning is even more promising than the successes to date in the systems literature.

URLhttp://www.usenix.org/event/hotpar09/tech/
AttachmentSize
Machine Learning321.59 KB